Prediction of transient thermo-mechanical behavior of the headstock assembly of a CNC lathe

Polymer concrete or epoxy granite is becoming more popular for beds, bases, and other structures of precision machine tools, owing to its excellent damping characteristics. To realize the same static rigidity as that of the cast-iron structures, steel-reinforced epoxy granite (SREG) structures are being used. The vast differences in the thermal properties of steel and epoxy granite (EG) are likely to cause higher magnitudes of thermal error. This work aims to investigate the thermal behaviour of a computerized numerical control (CNC) lathe built with a novel dynamically enhanced SREG bed and compare its performance with the lathe with a cast iron bed. Experimental and numerical investigations have been carried out under cross-feed (CF) drive idle running conditions to determine the TCP deformation. The results reveal that the thermal error in the CNC lathe with SREG bed is 1.68 times that of the lathe with cast iron (CI) bed at 20 ºC and 1.8 times at 40 ºC environmental temperature variation chamber (ETVC) conditions. It could be identified that the heat generated in the CF is conducted to the steel guideways embedded in the SREG bed, but further heat transfer to the EG portion of the bed is impeded, and hence the heat accumulation that occurs in the guideways leads to higher magnitude of the thermal error. The experimentally validated numerical model is used to extend the investigations to study the effect of the idle running of the longitudinal feed drive (LF) and combined cross and longitudinal feed drives, on the thermal behaviour of the lathe.

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“…The torque and frictional heat due to friction in the bearings is calculated by the given Eq. ( 1) [38]:…”

Section: Modelling Of Heat Sources With Analytical Heat Calculationsmentioning

confidence: 99%

Thermal Investigations on a CNC Lathe Fitted with a Dynamically Enhanced Steel-Reinforced Epoxy Granite Bed

Mula¹,

Thyla²,

Subramanian³

et al. 2023

sv-jme

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“…(2) Among these methods, thermal error compensation is not only easy but also efficient. (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) Thus, it is the most commonly used way of enhancing the machining accuracy of machine tools. Generally speaking, a thermal error compensation procedure includes the following steps.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, more parameters should be considered when setting experimental cutting conditions. Raja et al (7) and Wang et al (9) adopted the parameters of spindle rotational velocity and feeding speed in setting experimental cutting conditions. Note that no previous studies have systematically considered the effect of changing the parameters that influence the cutting conditions.…”

Section: Introductionmentioning

confidence: 99%

Applying Integrated Grey System Theory and Sensor Technology to Study Influence of Cutting Conditions on Thermal Error Modeling of Machine Tools

Wang¹,

Yang²,

Wu³

et al. 2021

Sensors and Materials

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To produce a good machine tool, the thermally induced error in the machine during machining plays a crucial role and is an important issue needing to be resolved. The thermal error may account for 70% of the total error. There are three main approaches to solving the thermal error problem: preventing heat flows from hot components, designing a thermally stable structure for the machine, and compensating the thermal error using thermal error models. The first two approaches can be carried out in the primary design stage of machine tools, and they have been used in the manufacture of commercial products. The third approach, the strategy of thermal error compensation, is the most effective and popular approach. However, there are still many unsolved problems. Among these problems, the cutting conditions have a significant influence on the modeling precision of the thermal error. In this study, we develop an integral model based on the integrated grey system theory (IGST) in conjunction with a genetic-algorithm (GA)-optimized back-propagation neural network (BPNN) to investigate the influence of cutting conditions on a machine tool's thermal error. The model is chosen on account of its high ability in dealing with a small amount of training data. Results show that a single thermal error modeling formula cannot make accurate predictions for different cutting conditions. Suitable adjustment of the modeling parameters or the use of a multiple modeling scheme is needed.

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“…Thermal error phenomenon will reduce the accuracy of processing and lead to poor quality. The main reason for the occurrence of thermal error phenomenon is because of friction between the bearings [3][4][5] which generates heat. 6,7 Deflection occurred when the bearings are heated, which accounts for 30-50% 8 of the total deflection.…”

Section: Introductionmentioning

confidence: 99%

Intelligent integrated monitoring system for lathe equipment

Jian

Hsieh

Guo

2019

Journal of Low Frequency Noise, Vibration and Active Control

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During the production process of the components of precision machines, a stable quality is the key consideration of the vendors. During the production process of metal parts, a thermal expansion error of the spindle will occur due to long time operations, which causes machining errors during the production process. The chatter phenomenon that occurs during the production of components causes knife marks on the processing surface. Thus, chatter is also a major factor that affects the stability of quality. The occurrence of chatter phenomenon not only affects the quality of metal components, it also causes severe wear of the blade surface and shortens the life of the knife tool, and makes the cutting quality more difficult to control. Therefore, this paper uses the lathe machine to simulate the production process in the factory, to collect signals of the thermal expansion error of the spindle phenomenon and the coexistence of chatter, for timely compensation of the thermal expansion error and the development of the chatter suppression system, while spending extra efforts to implement real-time dynamic detection with the Lorenz chaotic system and applying spindle speed selection to control the chatter phenomenon, thus enhancing the processing quality and lengthening the longevity of the knife tool. As for the compensation of temperature, the system applies multi-regression analysis to detect the front and rear bearings of the spindle and room temperature and to compensate the error of the spindle in real time. The system takes real-time control of the chatter phenomenon and compensation of long-time thermal expansion error as its core architecture, while the secondary architecture combines the Internet of Things with Skymars to enable the machine to stabilize communication and to combine with heterogeneous machines such as robotic arm, milling machine, and grinding machine. It is a framework with horizontal development and establishes a database to record the critical value of the occurrence of chatter and implement two-time quality detection, achieving management of the production line, improvement of quality, and cloud information storage.

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Prediction of transient thermo-mechanical behavior of the headstock assembly of a CNC lathe

Cited by 11 publications

References 11 publications

Thermal Investigations on a CNC Lathe Fitted with a Dynamically Enhanced Steel-Reinforced Epoxy Granite Bed

Thermal Investigations on a CNC Lathe Fitted with a Dynamically Enhanced Steel-Reinforced Epoxy Granite Bed

Applying Integrated Grey System Theory and Sensor Technology to Study Influence of Cutting Conditions on Thermal Error Modeling of Machine Tools

Intelligent integrated monitoring system for lathe equipment

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